Articles | Volume 17, issue 9
https://doi.org/10.5194/nhess-17-1683-2017
https://doi.org/10.5194/nhess-17-1683-2017
Research article
 | 
29 Sep 2017
Research article |  | 29 Sep 2017

Multi-variable flood damage modelling with limited data using supervised learning approaches

Dennis Wagenaar, Jurjen de Jong, and Laurens M. Bouwer

Viewed

Total article views: 4,402 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,208 2,065 129 4,402 185 97 110
  • HTML: 2,208
  • PDF: 2,065
  • XML: 129
  • Total: 4,402
  • Supplement: 185
  • BibTeX: 97
  • EndNote: 110
Views and downloads (calculated since 12 Jan 2017)
Cumulative views and downloads (calculated since 12 Jan 2017)

Viewed (geographical distribution)

Total article views: 4,402 (including HTML, PDF, and XML) Thereof 4,127 with geography defined and 275 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 24 Jun 2024
Download
Short summary
Flood damage models are an important component of cost–benefit analyses for flood protection measures. Currently flood damage models predict the flood damage often only based on water depth. Recently, some progress has been made in also including other variables for this prediction. Data-intensive approaches (machine learning) have been applied to do this. In practice the required data for this are rare. We apply these new approaches on a new type of dataset (combination of different sources).
Altmetrics
Final-revised paper
Preprint